Learning outcomes

At the end of the course, students will be able to: 

  • Use Python for data analytics.
  • Manipulate complex datasets and apply a set of various processes
  • Interrogate data and provide answers using statistics and graphical visualizations, examples: 
    • Does the number of rooms have a bigger impact than the total square footage on the house price? 
    • To what extent are bigger houses more expensive? Is the relationship linear or quadratic? 
    • Which is the chicest neighborhood in the city? Was it always the case? 
  • Develop a predictive model: 
    • A model to predict the price of a house based on different characteristics such as its square footage, its age, its neighborhood, etc. 

Goals

The objectives of this course are to provide students with the bases of the Python language for data analytics: (i) for the preparation of data, (ii) for the statistical analysis of data, and (iii) for the visualization of data. 

Content

Part 1. Introduction

  • Introduction to Data Analytics and Python

Part 2. Statistical Inference

  • Data Munging and Visualization
  • Descriptive Statistics & Statistical Inference
  • Case Study - Cycle Sharing Scheme

Part 3. Introduction to Machine Learning

  • Classification
  • Regression

Language of instruction

Français
Training Study programme Block Credits Mandatory
Bachelor in Business Engineering Standard 0 4
Bachelor in Business Engineering Standard 3 4